Machine learning unveils the complex nonlinearity of concrete materials’ uniaxial compressive strength-Reference-Cited by-同舟云学术

Machine learning unveils the complex nonlinearity of concrete materials’ uniaxial compressive strength

Published:2024-04-25 Issue: Volume: Page:1-15
ISSN:1562-3599
Container-title:International Journal of Construction Management
language:en
Short-container-title:International Journal of Construction Management

Author:

Pandey Siddhi¹,Paudel Satish²,Devkota Kabin³,Kshetri Kushum⁴,Asteris Panagiotis G.⁵

Affiliation:

1. Tundi Power Company, Lazimpath, Nepal

2. Department of Civil and Environmental Engineering, University of NV, Reno, NV, USA

3. Kantipur Engineering College, Institute of Engineering, Tribhuvan University, Dhapakhel, Nepal

4. School of Management, Tribhuvan University, Kirtipur, Nepal

5. School of Pedagogical and Technological Education, Computational Mechanics Laboratory, Athens, Greece

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15623599.2024.2345008

Reference65 articles.

1. Compressive strength prediction of Portland cement concrete with age using a new model

2. ACI 318R-19. 2019. Building code requirements for structural concrete: (ACI 318-19) and commentary (ACI 318R-19).

3. Evaluation and comparison on performance evaluation of polymer brick utilizing plastic waste with local sourced burnt bricks;Adhikari B;I-Manager’s J Struct Eng,2023

4. Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

5. Innovative soft computing techniques including artificial neural network and nonlinear regression models to predict the compressive strength of environmentally friendly concrete incorporating waste glass powder

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